To address this, we propose a novel deep learning model for 5G base station energy consumption estimation based on a real-world dataset. Unlike existing methods, our approach integrates the Base
The fifth generation of the Radio Access Network (RAN) has brought new services, technologies, and paradigms with the corresponding societal benefits. However,
In the coming future due to the 5G network, the environmental sustainability and energy consumed by the femtocell BSs will turn into a big problem. Hence, effective strategies for diminishing the
This paper proposes a power control algorithm based on energy efficiency, which combines cell breathing technology and base station sleep technology to reduce base station energy consumption
· The high-energy consumption and high construction density of 5G base stations have greatly increased the demand for backup energy storage batteries.
An energy consumption optimization strategy of 5G base stations (BSs) considering variable threshold sleep mechanism (ECOS-BS) is proposed, which includes the initial matching
These 5G base stations consume about three times the power of the 4G stations. The main reason for this spike in power consumption is the addition of massive MIMO and beamforming,
Estimate Energy Consumption: Develop a machine learning model to accurately estimate the energy consumed by 5G base stations. The dataset includes various features related to the operation of 5G
Power consumption models for base stations are briefly discussed as part of the development of a model for life cycle assessment. An overview of relevant base station power
A new power model structure is proposed in order to assess the power consumption of traditional base stations, their extensions, and alternative architectures such as large-scale antenna...
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